Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations581460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory44.4 MiB
Average record size in memory80.0 B

Variable types

Numeric8
Categorical2

Alerts

gpu_index has constant value "0" Constant
pcie_link_width_current has constant value "16" Constant
memory_free_MiB is highly overall correlated with memory_used_MiBHigh correlation
memory_used_MiB is highly overall correlated with memory_free_MiBHigh correlation
power_draw_W is highly overall correlated with temperature_gpu and 3 other fieldsHigh correlation
temperature_gpu is highly overall correlated with power_draw_W and 3 other fieldsHigh correlation
temperature_memory is highly overall correlated with power_draw_W and 3 other fieldsHigh correlation
utilization_gpu_pct is highly overall correlated with power_draw_W and 3 other fieldsHigh correlation
utilization_memory_pct is highly overall correlated with power_draw_W and 3 other fieldsHigh correlation
memory_free_MiB is highly skewed (γ1 = 21.3560301) Skewed
memory_used_MiB is highly skewed (γ1 = -21.3560301) Skewed
timestamp is uniformly distributed Uniform
timestamp has unique values Unique
utilization_gpu_pct has 321543 (55.3%) zeros Zeros
utilization_memory_pct has 321861 (55.4%) zeros Zeros

Reproduction

Analysis started2025-05-16 13:22:56.436750
Analysis finished2025-05-16 13:23:14.331910
Duration17.9 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

timestamp
Real number (ℝ)

Uniform  Unique 

Distinct581460
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6181108 × 109
Minimum1.6180808 × 109
Maximum1.6181409 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:14.602010image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.6180808 × 109
5-th percentile1.6180838 × 109
Q11.6180958 × 109
median1.6181108 × 109
Q31.6181258 × 109
95-th percentile1.6181378 × 109
Maximum1.6181409 × 109
Range60039.384
Interquartile range (IQR)30015.549

Descriptive statistics

Standard deviation17329.87
Coefficient of variation (CV)1.070994 × 10-5
Kurtosis-1.1999235
Mean1.6181108 × 109
Median Absolute Deviation (MAD)15007.801
Skewness4.6920816 × 10-5
Sum9.4086672 × 1014
Variance3.0032441 × 108
MonotonicityStrictly increasing
2025-05-16T15:23:14.728815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1618080815 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120837 1
 
< 0.1%
1618120836 1
 
< 0.1%
Other values (581450) 581450
> 99.9%
ValueCountFrequency (%)
1618080815 1
< 0.1%
1618080815 1
< 0.1%
1618080815 1
< 0.1%
1618080815 1
< 0.1%
1618080815 1
< 0.1%
1618080815 1
< 0.1%
1618080815 1
< 0.1%
1618080816 1
< 0.1%
1618080816 1
< 0.1%
1618080816 1
< 0.1%
ValueCountFrequency (%)
1618140854 1
< 0.1%
1618140854 1
< 0.1%
1618140854 1
< 0.1%
1618140854 1
< 0.1%
1618140854 1
< 0.1%
1618140854 1
< 0.1%
1618140854 1
< 0.1%
1618140853 1
< 0.1%
1618140853 1
< 0.1%
1618140853 1
< 0.1%

gpu_index
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
0
581460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters581460
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 581460
100.0%

Length

2025-05-16T15:23:14.982755image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-16T15:23:15.078771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 581460
100.0%

Most occurring characters

ValueCountFrequency (%)
0 581460
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 581460
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 581460
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 581460
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 581460
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 581460
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 581460
100.0%

utilization_gpu_pct
Real number (ℝ)

High correlation  Zeros 

Distinct37
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.307722
Minimum0
Maximum36
Zeros321543
Zeros (%)55.3%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:15.162328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333
95-th percentile34
Maximum36
Range36
Interquartile range (IQR)33

Descriptive statistics

Standard deviation16.170694
Coefficient of variation (CV)1.1302074
Kurtosis-1.8833926
Mean14.307722
Median Absolute Deviation (MAD)0
Skewness0.27769445
Sum8319368
Variance261.49133
MonotonicityNot monotonic
2025-05-16T15:23:15.278248image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 321543
55.3%
33 81445
 
14.0%
34 74708
 
12.8%
32 35944
 
6.2%
35 24159
 
4.2%
31 10118
 
1.7%
30 5196
 
0.9%
29 3646
 
0.6%
28 2409
 
0.4%
27 1872
 
0.3%
Other values (27) 20420
 
3.5%
ValueCountFrequency (%)
0 321543
55.3%
1 91
 
< 0.1%
2 136
 
< 0.1%
3 85
 
< 0.1%
4 153
 
< 0.1%
5 122
 
< 0.1%
6 87
 
< 0.1%
7 202
 
< 0.1%
8 164
 
< 0.1%
9 227
 
< 0.1%
ValueCountFrequency (%)
36 1367
 
0.2%
35 24159
 
4.2%
34 74708
12.8%
33 81445
14.0%
32 35944
6.2%
31 10118
 
1.7%
30 5196
 
0.9%
29 3646
 
0.6%
28 2409
 
0.4%
27 1872
 
0.3%

utilization_memory_pct
Real number (ℝ)

High correlation  Zeros 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.061834
Minimum0
Maximum8
Zeros321861
Zeros (%)55.4%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:15.379725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37
95-th percentile8
Maximum8
Range8
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.4691596
Coefficient of variation (CV)1.1330332
Kurtosis-1.8668676
Mean3.061834
Median Absolute Deviation (MAD)0
Skewness0.28792577
Sum1780334
Variance12.035068
MonotonicityNot monotonic
2025-05-16T15:23:15.478400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 321861
55.4%
7 202031
34.7%
8 29632
 
5.1%
6 11291
 
1.9%
3 5347
 
0.9%
5 5209
 
0.9%
4 3781
 
0.7%
2 1797
 
0.3%
1 511
 
0.1%
ValueCountFrequency (%)
0 321861
55.4%
1 511
 
0.1%
2 1797
 
0.3%
3 5347
 
0.9%
4 3781
 
0.7%
5 5209
 
0.9%
6 11291
 
1.9%
7 202031
34.7%
8 29632
 
5.1%
ValueCountFrequency (%)
8 29632
 
5.1%
7 202031
34.7%
6 11291
 
1.9%
5 5209
 
0.9%
4 3781
 
0.7%
3 5347
 
0.9%
2 1797
 
0.3%
1 511
 
0.1%
0 321861
55.4%

memory_free_MiB
Real number (ℝ)

High correlation  Skewed 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31024.263
Minimum31021
Maximum32510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:15.591411image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum31021
5-th percentile31021
Q131021
median31021
Q331021
95-th percentile31021
Maximum32510
Range1489
Interquartile range (IQR)0

Descriptive statistics

Standard deviation67.635304
Coefficient of variation (CV)0.0021800777
Kurtosis460.13693
Mean31024.263
Median Absolute Deviation (MAD)0
Skewness21.35603
Sum1.8039368 × 1010
Variance4574.5343
MonotonicityNot monotonic
2025-05-16T15:23:15.716080image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
31021 579993
99.7%
32510 1134
 
0.2%
31483 145
 
< 0.1%
31921 80
 
< 0.1%
31277 27
 
< 0.1%
31679 25
 
< 0.1%
32051 14
 
< 0.1%
31373 2
 
< 0.1%
31123 1
 
< 0.1%
31169 1
 
< 0.1%
Other values (38) 38
 
< 0.1%
ValueCountFrequency (%)
31021 579993
99.7%
31123 1
 
< 0.1%
31169 1
 
< 0.1%
31275 1
 
< 0.1%
31277 27
 
< 0.1%
31373 2
 
< 0.1%
31457 1
 
< 0.1%
31481 1
 
< 0.1%
31483 145
 
< 0.1%
31485 1
 
< 0.1%
ValueCountFrequency (%)
32510 1134
0.2%
32506 1
 
< 0.1%
32494 1
 
< 0.1%
32201 1
 
< 0.1%
32197 1
 
< 0.1%
32195 1
 
< 0.1%
32181 1
 
< 0.1%
32159 1
 
< 0.1%
32143 1
 
< 0.1%
32121 1
 
< 0.1%

memory_used_MiB
Real number (ℝ)

High correlation  Skewed 

Distinct48
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1485.7372
Minimum0
Maximum1489
Zeros1134
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:15.837271image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1489
Q11489
median1489
Q31489
95-th percentile1489
Maximum1489
Range1489
Interquartile range (IQR)0

Descriptive statistics

Standard deviation67.635304
Coefficient of variation (CV)0.045523059
Kurtosis460.13693
Mean1485.7372
Median Absolute Deviation (MAD)0
Skewness-21.35603
Sum8.6389677 × 108
Variance4574.5343
MonotonicityNot monotonic
2025-05-16T15:23:15.960327image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
1489 579993
99.7%
0 1134
 
0.2%
1027 145
 
< 0.1%
589 80
 
< 0.1%
1233 27
 
< 0.1%
831 25
 
< 0.1%
459 14
 
< 0.1%
1137 2
 
< 0.1%
1387 1
 
< 0.1%
1341 1
 
< 0.1%
Other values (38) 38
 
< 0.1%
ValueCountFrequency (%)
0 1134
0.2%
4 1
 
< 0.1%
16 1
 
< 0.1%
309 1
 
< 0.1%
313 1
 
< 0.1%
315 1
 
< 0.1%
329 1
 
< 0.1%
351 1
 
< 0.1%
367 1
 
< 0.1%
389 1
 
< 0.1%
ValueCountFrequency (%)
1489 579993
99.7%
1387 1
 
< 0.1%
1341 1
 
< 0.1%
1235 1
 
< 0.1%
1233 27
 
< 0.1%
1137 2
 
< 0.1%
1053 1
 
< 0.1%
1029 1
 
< 0.1%
1027 145
 
< 0.1%
1025 1
 
< 0.1%

temperature_gpu
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.21975
Minimum34
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:16.315866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile35
Q137
median38
Q340
95-th percentile42
Maximum43
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.1316529
Coefficient of variation (CV)0.055773596
Kurtosis-0.77239838
Mean38.21975
Median Absolute Deviation (MAD)2
Skewness0.090181905
Sum22223256
Variance4.5439441
MonotonicityNot monotonic
2025-05-16T15:23:16.401705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
39 98333
16.9%
38 92768
16.0%
37 82636
14.2%
40 73179
12.6%
36 73146
12.6%
35 55912
9.6%
41 50633
8.7%
42 34986
 
6.0%
34 12381
 
2.1%
43 7486
 
1.3%
ValueCountFrequency (%)
34 12381
 
2.1%
35 55912
9.6%
36 73146
12.6%
37 82636
14.2%
38 92768
16.0%
39 98333
16.9%
40 73179
12.6%
41 50633
8.7%
42 34986
 
6.0%
43 7486
 
1.3%
ValueCountFrequency (%)
43 7486
 
1.3%
42 34986
 
6.0%
41 50633
8.7%
40 73179
12.6%
39 98333
16.9%
38 92768
16.0%
37 82636
14.2%
36 73146
12.6%
35 55912
9.6%
34 12381
 
2.1%

temperature_memory
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.632155
Minimum33
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:16.488754image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile34
Q136
median38
Q339
95-th percentile41
Maximum43
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.061444
Coefficient of variation (CV)0.054778791
Kurtosis-0.76945179
Mean37.632155
Median Absolute Deviation (MAD)2
Skewness0.090745002
Sum21881593
Variance4.2495512
MonotonicityNot monotonic
2025-05-16T15:23:16.576520image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
38 105969
18.2%
37 90530
15.6%
39 86391
14.9%
36 77065
13.3%
35 72480
12.5%
40 57720
9.9%
41 41474
 
7.1%
34 33400
 
5.7%
42 15245
 
2.6%
33 1135
 
0.2%
ValueCountFrequency (%)
33 1135
 
0.2%
34 33400
 
5.7%
35 72480
12.5%
36 77065
13.3%
37 90530
15.6%
38 105969
18.2%
39 86391
14.9%
40 57720
9.9%
41 41474
 
7.1%
42 15245
 
2.6%
ValueCountFrequency (%)
43 51
 
< 0.1%
42 15245
 
2.6%
41 41474
 
7.1%
40 57720
9.9%
39 86391
14.9%
38 105969
18.2%
37 90530
15.6%
36 77065
13.3%
35 72480
12.5%
34 33400
 
5.7%

power_draw_W
Real number (ℝ)

High correlation 

Distinct3232
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.563626
Minimum25.89
Maximum73.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.4 MiB
2025-05-16T15:23:16.680713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum25.89
5-th percentile37.83
Q138.15
median38.78
Q361.15
95-th percentile66.99
Maximum73.14
Range47.25
Interquartile range (IQR)23

Descriptive statistics

Standard deviation12.066106
Coefficient of variation (CV)0.24845973
Kurtosis-1.6914574
Mean48.563626
Median Absolute Deviation (MAD)0.95
Skewness0.3788958
Sum28237806
Variance145.59091
MonotonicityNot monotonic
2025-05-16T15:23:16.802784image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38.02 19553
 
3.4%
38.31 14670
 
2.5%
38.4 12565
 
2.2%
37.93 8882
 
1.5%
38.03 8805
 
1.5%
37.94 8743
 
1.5%
38.21 8283
 
1.4%
37.92 8174
 
1.4%
38.49 8171
 
1.4%
38.11 7361
 
1.3%
Other values (3222) 476253
81.9%
ValueCountFrequency (%)
25.89 1
 
< 0.1%
25.93 6
 
< 0.1%
25.94 3
 
< 0.1%
26 3
 
< 0.1%
26.01 575
0.1%
26.02 13
 
< 0.1%
26.03 82
 
< 0.1%
26.04 3
 
< 0.1%
26.05 1
 
< 0.1%
26.08 1
 
< 0.1%
ValueCountFrequency (%)
73.14 2
< 0.1%
73.04 1
 
< 0.1%
72.92 1
 
< 0.1%
72.76 3
< 0.1%
72.66 2
< 0.1%
72.56 1
 
< 0.1%
72.54 1
 
< 0.1%
72.52 1
 
< 0.1%
72.48 1
 
< 0.1%
72.47 2
< 0.1%

pcie_link_width_current
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 MiB
16
581460 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1162920
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row16
2nd row16
3rd row16
4th row16
5th row16

Common Values

ValueCountFrequency (%)
16 581460
100.0%

Length

2025-05-16T15:23:16.911644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-16T15:23:16.988745image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
16 581460
100.0%

Most occurring characters

ValueCountFrequency (%)
1 581460
50.0%
6 581460
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1162920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 581460
50.0%
6 581460
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1162920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 581460
50.0%
6 581460
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1162920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 581460
50.0%
6 581460
50.0%

Interactions

2025-05-16T15:23:11.744622image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:02.980046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.299805image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.357652image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.409207image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:07.508246image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.733916image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.737538image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:11.875094image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:03.379679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.433133image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.493982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.543958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:08.772715image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.863485image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.866125image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:12.004791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:03.513683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.566960image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.625054image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.677367image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:08.965578image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.991875image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.996135image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:12.138836image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:03.652258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.704885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.763671image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.812596image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.101653image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.124073image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:11.127328image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:12.269281image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:03.787032image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.848501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.898862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.945301image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.231092image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.252662image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:11.258350image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:12.400564image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:03.920241image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.983848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.033783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:07.079409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.365042image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.382501image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:11.387571image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:12.521517image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.044893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.107844image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.158566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:07.204086image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.487586image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.501612image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:11.504585image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:12.640307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:04.168461image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:05.233576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:06.283675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:07.370639image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:09.610320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:10.619111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-05-16T15:23:11.623061image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-05-16T15:23:17.041543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
memory_free_MiBmemory_used_MiBpower_draw_Wtemperature_gputemperature_memorytimestamputilization_gpu_pctutilization_memory_pct
memory_free_MiB1.000-1.000-0.084-0.084-0.082-0.069-0.041-0.044
memory_used_MiB-1.0001.0000.0840.0840.0820.0690.0410.044
power_draw_W-0.0840.0841.0000.7910.747-0.2110.8300.844
temperature_gpu-0.0840.0840.7911.0000.973-0.3890.5810.601
temperature_memory-0.0820.0820.7470.9731.000-0.3950.5160.535
timestamp-0.0690.069-0.211-0.389-0.3951.000-0.061-0.067
utilization_gpu_pct-0.0410.0410.8300.5810.516-0.0611.0000.972
utilization_memory_pct-0.0440.0440.8440.6010.535-0.0670.9721.000

Missing values

2025-05-16T15:23:12.762801image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-16T15:23:13.148121image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timestampgpu_indexutilization_gpu_pctutilization_memory_pctmemory_free_MiBmemory_used_MiBtemperature_gputemperature_memorypower_draw_Wpcie_link_width_current
01.618081e+09000325100343426.1016
11.618081e+09000325100343426.0116
21.618081e+09000325100343426.0116
31.618081e+09000325100343426.0116
41.618081e+09000325100343426.1016
51.618081e+09000325100343426.2016
61.618081e+09000325100343426.1016
71.618081e+09000325100343426.1016
81.618081e+09000325100343426.0116
91.618081e+09000325100343426.1016
timestampgpu_indexutilization_gpu_pctutilization_memory_pctmemory_free_MiBmemory_used_MiBtemperature_gputemperature_memorypower_draw_Wpcie_link_width_current
5814501.618141e+09000314831027353437.7816
5814511.618141e+09000314831027353437.8816
5814521.618141e+09000314831027343437.7916
5814531.618141e+09000314831027353437.7016
5814541.618141e+09000314831027343437.7716
5814551.618141e+09000314831027343437.7916
5814561.618141e+09000314831027343437.7716
5814571.618141e+09000314851025343437.8616
5814581.618141e+090201325100353437.7816
5814591.618141e+090201325100343437.7716